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What an ML-ful World! MLKit for Android dev.
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Britt Barak
October 12, 2018
Programming
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What an ML-ful World! MLKit for Android dev.
Britt Barak
October 12, 2018
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Transcript
What an ML-ful world Britt Barak
Once upon a time @BrittBarak
beta @BrittBarak
ML Capability ?! @BrittBarak
Who is afraid of Machine Learning? & First Steps With
ML-Kit @BrittBarak
Britt Barak Developer Experience, Nexmo Google Developer Expert Britt Barak
@brittBarak
None
@BrittBarak
= @BrittBarak
§ What’s the difference? @BrittBarak
…and classify? @BrittBarak
@BrittBarak
This is a strawberry @BrittBarak
This is a strawberry Red Seeds pattern Narrow top leaves
@BrittBarak Pointy at the bottom Round at the top
Strawberry Not Not Not Strawberry Strawberry Not Not Not @BrittBarak
~*~ images ~*~ @BrittBarak
@BrittBarak Vision library
Text Recognition @BrittBarak
Face Detection @BrittBarak
Barcode Scanning @BrittBarak
Image Labelling @BrittBarak
Landmark Recognition @BrittBarak
Custom Models @BrittBarak
Example @BrittBarak
@BrittBarak
@BrittBarak
Detector detector .execute(image) Result: @BrittBarak “Ben & Jerry’s pistachio ice
cream”
1. Setup Detector @BrittBarak
Local or cloud? @BrittBarak
@BrittBarak
Local •Realtime •Offline support •Security / Privacy •Bandwith •… @BrittBarak
Cloud •More accuracy & features •But more latency •Pricing @BrittBarak
1. Setup Detector @BrittBarak
Text Detector textDetector = FirebaseVision.getInstance() @BrittBarak
Text Detector textDetector = FirebaseVision.getInstance() .onDeviceTextRecognizer @BrittBarak
Text Detector textDetector = FirebaseVision.getInstance() .cloudTextRecognizer @BrittBarak
2. Process input @BrittBarak
FirebaseVisionImage •Bitmap •image Uri •Media Image •byteArray •byteBuffer @BrittBarak
image = FirebaseVisionImage.fromBitmap(bitmap) @BrittBarak Text Detector
3. Execute the model @BrittBarak
Text Detector textDetector.processImage(image) @BrittBarak
Text Detector textDetector.processImage(image) .addOnSuccessListener { } @BrittBarak
Text Detector textDetector.processImage(image) .addOnSuccessListener { firebaseVisionTexts -> processOutput(fbVisionTexts) } @BrittBarak
4. Process output @BrittBarak
firebaseVisionTexts.text @BrittBarak
someTextView.text = firebaseVisionTexts.text @BrittBarak UI
Result @BrittBarak
Result @BrittBarak
(another) Example : Labelling @BrittBarak
Detector detector .execute(image) Result: @BrittBarak ice cream pint
Vegetables Deserts Vegetables
1. Setup Detector @BrittBarak
Image Classifier imageDetector = FirebaseVision.getInstance() @BrittBarak
Image Classifier imageDetector = FirebaseVision.getInstance() .visionLabelDetector @BrittBarak
Image Classifier imageDetector = FirebaseVision.getInstance .visionCloudLabelDetector @BrittBarak
2. Process input @BrittBarak
image = FirebaseVisionImage.fromBitmap(bitmap) @BrittBarak Image Classifier
3. Execute the model @BrittBarak
Image Classifier imageDetector.detectInImage(image) @BrittBarak
Image Classifier imageDetector.detectInImage(image) .addOnSuccessListener{ } @BrittBarak
Image Classifier imageDetector.detectInImage(image) .addOnSuccessListener{ fBLabels -> processOutput(fBLabels) } @BrittBarak
4. Process output @BrittBarak
fbLabel.label fbLabel.confidence fbLabel.entityId @BrittBarak
UI for (fbLabel in labels) { s = "${fbLabel.label} :
${fbLabel.confidence}" } @BrittBarak
Result
Result
It is an ML-ful world Enjoy!
Thank you! Keep in touch!